Accuracy assessment of land cover products in China from 2000 to 2020

Zhiwen LI, Xingyu CHEN, Jie QI, Chong XU, Jiafu AN, Jiandong CHEN

Research output: Journal PublicationsJournal Article (refereed)peer-review

4 Citations (Scopus)


The accuracy assessment of land cover data is of significant value to accurately monitor and objectively reproduce spatio-temporal dynamic changes to land surface landscapes. In this study, the interpretation and applicability of CCI, MCD, and CGLS long time-series land cover data products for China were evaluated via consistency analysis and a confusion matrix system using NLUD-C periodic products as reference data. The results showed that CGLS had the highest overall accuracy, Kappa coefficient, and area consistency in the continuous time-series evaluation, followed by MCD, whereas CCI had the worst performance. For the accuracy assessment of subdivided land cover types, the three products could accurately describe the distribution of forest land in China with a high recognition level, but their recognition ability for water body and construction land was poor. Among the other types, CCI could better identify cropland, MCD for grassland, and CGLS for unused land. Based on these evaluation results and characteristics of the data products, we developed suitable selection schemes for users with different requirements.
Original languageEnglish
Article number12936 (2023)
Number of pages1
JournalScientific Reports
Issue number1
Publication statusPublished - Dec 2023

Bibliographical note

Funding Information:
This study was supported by the Key Program of the National Social Science Foundation of China [grant number 21ATJ008], the National Natural Science Foundation of China [grant numbers 71934001, 71471001, 41771568, and 71533004], the National Key Research and Development Program of China [grant number 2016YFA0602500], the Strategic Priority Research Program of Chinese Academy of Sciences [grant number XDA23070400], and the Sichuan Province Social Science High Level Research Team Building.

Publisher Copyright:
© 2023, Springer Nature Limited.


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